AITPM Email Newsletter Volume 0911, November 2009 : President's Message

President’s Report Hello fellow AITPM members, It is interesting to follow the news at present, where transport costs are getting a significant airing. Treasury Secretary Dr Ken Henry has enunciated something Australians may have considered extremely radical just a few years back, but in the present time appears to quite a few to be a realistic alternative. That being a rethink of the way we are charged for using our vehicles. It appears that serious consideration is being given to congestion charging, perhaps in place at least to some extent, of fuel excise. As a transport professional I am pleased that the debate has elevated to the national level, and would look forward that AITPM might contribute appropriately to it. As a motorist though, I naturally have my concerns about being hit in the hip pocket. Not that I actually drive during congested periods very much. I am fortunate to live five minutes either side of two well serviced bus corridors, one of which will eventually become a busway, and work in the central business district, which is hub from all spokes in Brisbane. As such, bus and foot are my preferred commute modes. Ah but I should not gloat, as my smart card fare is about to increase by 20 percent in the New Year! And if the newspapers are to be believed, further substantial increments are proposed over the coming few years. This is reported to recoup some more of the costs of actually providing the quality public transport system that we enjoy in our region. So I expect it will be very interesting to see how transport economics will play out in reality in the coming few years, and how governments cater to Australians who either cannot afford substantial increases in transport costs or have no viable alternatives to those facilities whose costs will increase. The 2010 AITPM National Conference, “What’s New?”, still has the opportunity for authors to submit an abstract for consideration so please consider how you might contribute to the event. Best regards to all, Jon Bunker

Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

An effective model of using negative relevance feedback for information filtering

Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Tag based collaborative filtering for recommender systems

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user’s important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Gifted young adolescents and email : developing a personal language of self

This paper reports on the use of email as a means to access the self-constructions of gifted young adolescents. Australian research shows that gifted young adolescents may feel more lonely and misunderstood than their same-age counterparts, yet they are seldom asked about their lives. Emerging use of online methods as a means of access to individual lives and perceptions has demonstrated the potential offered by the creation of digital texts as narrative data. Details are given of a qualitative study that engaged twelve children aged between 10 and 14 years, who were screened for giftedness, in a project involving the generation of emailed journal entries sent over a period of 6 months. With emphasis on participatory principles, individual young adolescents produced self-managed journal entries that were written and sent to the researcher from personal computers outside the school setting. Drawing from a theoretical understanding of self as constructed within dialogic relationships, the digital setting of email is proposed as a narrative space that fosters healthy self-disclosure. This paper outlines the benefits of using email as a means to explore emotions, promote reflective accounts of self and support the development of a personal language for self-expression. Individual excerpts will be presented to show that the harnessing of personal narratives within an email context has potential to yield valuable insights into the emotions, personal realities and experiences of gifted young adolescents. Findings will be presented to show that the co-construction of self-expressive and explanatory narratives supported by a facilitative adult listener promoted healthy self-awareness amongst participants. This paper contributes to appreciative conversations about using online methods as a flexible and practical avenue for conducting educational research. Furthermore, digital writing in email form will be presented as having distinct advantages over face-to-face methods when utilised with gifted young adolescents who may be unwilling to disclose information within school-based settings.